Resource Scheduling with Permutation Based Representations: Three Applications

Resource based scheduling using permutation based representations is reviewed. Permutation based representations are used in conjunction with genetic algo- rithms and local search algorithms for solving three very different scheduling problems. First, the Coors warehouse scheduling problem involves finding a per- mutation of customer orders that minimizes the average time that customers' orders spend at the loading docks while at the same time minimizing the running average inventory. Second, scheduling the Air Force Satellite Control Network (AFSCN) involves scheduling customer requests for contact time with a satellite via a ground station, where slot times on a ground station is the limited resource. The third application is scheduling the tracking of objects in space using ground based radar systems. Both satellites and debris in space must be tracked on reg- ular basis to maintain knowledge about the location and orbit of the object. The ground based radar system is the limited resource, but unlike AFSCN scheduling, this application involves significant uncertainty.

[1]  L. Darrell Whitley,et al.  The GENITOR Algorithm and Selection Pressure: Why Rank-Based Allocation of Reproductive Trials is Best , 1989, ICGA.

[2]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[3]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[4]  Adele E. Howe,et al.  Understanding Algorithm Performance on an Oversubscribed Scheduling Application , 2006, J. Artif. Intell. Res..

[5]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[6]  Lawrence Davis,et al.  Applying Adaptive Algorithms to Epistatic Domains , 1985, IJCAI.

[7]  Andrew M. Sutton,et al.  Using Adaptive Priority Weighting to Direct Search in Probabilistic Scheduling , 2007, ICAPS.

[8]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[9]  L. Darrell Whitley,et al.  Scheduling Problems and Traveling Salesmen: The Genetic Edge Recombination Operator , 1989, International Conference on Genetic Algorithms.

[10]  L. Darrell Whitley,et al.  Scheduling Space–Ground Communications for the Air Force Satellite Control Network , 2004, J. Sched..

[11]  Felix R. Hoots,et al.  Models for Propagation of NORAD Element Sets , 1980 .

[12]  David E. Goldberg,et al.  Alleles, loci and the traveling salesman problem , 1985 .

[13]  G. Syswerda,et al.  Schedule Optimization Using Genetic Algorithms , 1991 .

[14]  Shigenobu Kobayashi,et al.  Edge Assembly Crossover: A High-Power Genetic Algorithm for the Travelling Salesman Problem , 1997, ICGA.

[15]  Gilbert Syswerda,et al.  The Application of Genetic Algorithms to Resource Scheduling , 1991, International Conference on Genetic Algorithms.

[16]  L. Darrell Whitley,et al.  A Comparison of Genetic Sequencing Operators , 1991, ICGA.

[17]  Lawrence Davis,et al.  Job Shop Scheduling with Genetic Algorithms , 1985, ICGA.

[18]  Donald A. Parish A Genetic Algorithm Approach to Automating Satellite Range Scheduling , 1994 .

[19]  John L. Bresina,et al.  Expected Solution Quality , 1995, IJCAI.

[20]  Felix R. Hoots,et al.  SPACETRACK REPORT NO. 3 Models for Propagation of , 1988 .

[21]  Jean-Paul Watson,et al.  The impact of approximate evaluation on the performance of search algorithms for warehouse scheduling , 1999 .

[22]  Darrell Whitley,et al.  Modeling Permutation En-codings in Simple Genetic Algorithm , 1995 .

[23]  David E. Goldberg,et al.  Genetic Algorithms and the Variance of Fitness , 1991, Complex Syst..